#import the required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.express as px
import plotly.graph_objs as go
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from imblearn.combine import SMOTEENN
# Feature Processing (Scikit-learn processing, etc. )
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from collections import Counter
from imblearn.over_sampling import RandomOverSampler
import scipy.stats as stats
from scipy.stats import chi2_contingency
# Machine Learning (Scikit-learn Estimators, Catboost, LightGBM, etc. )
from sklearn.datasets import make_classification
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, fbeta_score
from sklearn.metrics import confusion_matrix
# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )
from sklearn.model_selection import KFold, cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor
# Other packages
from tabulate import tabulate
import os, pickle
import warnings
warnings.filterwarnings('ignore')
import pickle
C:\Users\acer\anaconda3\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
voda = pd.read_csv('Telco-Customer-Churn.csv')
voda.sample(50)
| customerID | gender | SeniorCitizen | Partner | Dependents | tenure | PhoneService | MultipleLines | InternetService | OnlineSecurity | ... | DeviceProtection | TechSupport | StreamingTV | StreamingMovies | Contract | PaperlessBilling | PaymentMethod | MonthlyCharges | TotalCharges | Churn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4956 | 3217-FZDMN | Female | 1 | No | No | 8 | Yes | No | Fiber optic | No | ... | Yes | No | Yes | Yes | Month-to-month | Yes | Credit card (automatic) | 94.45 | 742.95 | Yes |
| 6815 | 0270-THENM | Male | 0 | Yes | Yes | 72 | Yes | Yes | DSL | Yes | ... | Yes | Yes | No | No | Two year | No | Bank transfer (automatic) | 69.85 | 5102.35 | No |
| 3292 | 7284-BUYEC | Female | 0 | No | No | 5 | No | No phone service | DSL | No | ... | No | No | Yes | Yes | Month-to-month | Yes | Credit card (automatic) | 50.95 | 229.4 | No |
| 6625 | 3398-FSHON | Female | 1 | No | No | 12 | Yes | Yes | Fiber optic | No | ... | No | No | Yes | No | Month-to-month | Yes | Electronic check | 91.30 | 1094.5 | Yes |
| 1383 | 3334-CTHOL | Female | 0 | No | No | 1 | Yes | Yes | DSL | No | ... | No | No | No | No | Month-to-month | Yes | Bank transfer (automatic) | 49.95 | 49.95 | Yes |
| 5018 | 6928-ONTRW | Female | 0 | Yes | Yes | 72 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Two year | No | Credit card (automatic) | 19.70 | 1379.8 | No |
| 3410 | 4918-QLLIW | Male | 0 | No | No | 3 | Yes | No | DSL | No | ... | No | No | Yes | No | Month-to-month | No | Credit card (automatic) | 53.40 | 188.7 | Yes |
| 2377 | 9308-ANMVE | Male | 0 | No | Yes | 47 | Yes | No | DSL | Yes | ... | No | No | No | No | Month-to-month | Yes | Electronic check | 55.30 | 2654.05 | No |
| 1647 | 5442-XSDCW | Male | 0 | Yes | Yes | 11 | Yes | No | Fiber optic | No | ... | No | No | Yes | No | Month-to-month | Yes | Bank transfer (automatic) | 79.50 | 868.5 | Yes |
| 1934 | 4587-NUKOX | Female | 0 | No | No | 3 | Yes | No | Fiber optic | Yes | ... | No | No | No | No | Month-to-month | Yes | Electronic check | 79.10 | 246.5 | Yes |
| 4432 | 3891-NLXJB | Male | 0 | No | No | 37 | No | No phone service | DSL | Yes | ... | No | Yes | No | No | Two year | Yes | Mailed check | 40.55 | 1390.85 | No |
| 6263 | 8409-WQJUX | Female | 0 | No | No | 25 | No | No phone service | DSL | No | ... | Yes | Yes | Yes | Yes | One year | No | Electronic check | 54.20 | 1423.15 | No |
| 3059 | 7503-EPSZW | Female | 0 | Yes | Yes | 64 | Yes | Yes | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Two year | Yes | Mailed check | 24.05 | 1559.15 | No |
| 5776 | 4291-TPNFG | Male | 0 | Yes | No | 72 | Yes | Yes | DSL | Yes | ... | Yes | Yes | No | Yes | Two year | Yes | Bank transfer (automatic) | 82.30 | 5980.55 | No |
| 468 | 8896-RAZCR | Female | 0 | No | Yes | 44 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Two year | No | Mailed check | 19.90 | 868.1 | No |
| 5799 | 8066-POXGX | Female | 0 | No | No | 13 | No | No phone service | DSL | No | ... | No | No | No | Yes | Month-to-month | Yes | Electronic check | 35.10 | 446.1 | Yes |
| 69 | 7410-OIEDU | Male | 0 | No | No | 10 | Yes | No | Fiber optic | Yes | ... | Yes | No | No | No | Month-to-month | Yes | Mailed check | 79.85 | 887.35 | No |
| 280 | 0314-TKOSI | Female | 0 | No | No | 6 | Yes | No | DSL | Yes | ... | No | No | No | No | Month-to-month | No | Mailed check | 55.15 | 322.9 | No |
| 2094 | 1057-FOGLZ | Female | 0 | No | No | 18 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Month-to-month | No | Mailed check | 19.65 | 391.7 | No |
| 1180 | 4835-YSJMR | Male | 0 | No | No | 39 | Yes | No | DSL | No | ... | No | Yes | No | No | Two year | Yes | Bank transfer (automatic) | 49.80 | 1971.15 | No |
| 1019 | 9391-TTOYH | Female | 0 | No | No | 23 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Month-to-month | Yes | Mailed check | 19.50 | 470.2 | No |
| 4170 | 0906-QVPMS | Male | 0 | Yes | No | 72 | Yes | Yes | Fiber optic | Yes | ... | Yes | Yes | Yes | Yes | Two year | No | Bank transfer (automatic) | 115.15 | 8349.45 | No |
| 4447 | 7821-DPRQE | Male | 0 | Yes | No | 68 | Yes | Yes | Fiber optic | Yes | ... | No | Yes | Yes | Yes | Month-to-month | Yes | Electronic check | 107.70 | 7320.9 | No |
| 2730 | 1169-SAOCL | Male | 0 | No | No | 49 | Yes | Yes | Fiber optic | No | ... | No | Yes | Yes | Yes | One year | Yes | Bank transfer (automatic) | 106.65 | 5168.1 | No |
| 3800 | 7973-DZRKH | Female | 0 | No | Yes | 66 | Yes | Yes | Fiber optic | Yes | ... | Yes | Yes | No | No | Two year | Yes | Credit card (automatic) | 90.95 | 5930.05 | No |
| 4485 | 7975-JMZNT | Male | 0 | Yes | No | 66 | Yes | Yes | DSL | Yes | ... | Yes | Yes | Yes | Yes | Two year | No | Bank transfer (automatic) | 91.70 | 6075.9 | No |
| 5500 | 7139-JZFVG | Male | 0 | Yes | Yes | 60 | Yes | No | DSL | Yes | ... | Yes | No | No | No | Two year | No | Bank transfer (automatic) | 60.50 | 3694.45 | No |
| 1961 | 4445-KWOKW | Female | 0 | No | No | 42 | Yes | Yes | DSL | Yes | ... | No | No | No | No | One year | Yes | Bank transfer (automatic) | 60.15 | 2421.6 | No |
| 4754 | 2072-ZVJJX | Male | 0 | Yes | No | 68 | Yes | Yes | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Two year | No | Bank transfer (automatic) | 25.25 | 1728.2 | No |
| 3526 | 9026-RNUJS | Male | 1 | No | No | 5 | Yes | No | DSL | No | ... | Yes | No | No | No | Month-to-month | No | Electronic check | 50.35 | 237.25 | Yes |
| 1605 | 7941-RCJOW | Male | 0 | No | No | 65 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Two year | Yes | Credit card (automatic) | 19.55 | 1218.65 | No |
| 1174 | 6994-ORCWG | Female | 0 | No | No | 14 | Yes | Yes | DSL | No | ... | No | No | No | No | One year | Yes | Mailed check | 54.25 | 773.2 | No |
| 2372 | 0730-BGQGF | Male | 0 | Yes | Yes | 71 | Yes | Yes | DSL | Yes | ... | Yes | Yes | Yes | Yes | Two year | No | Credit card (automatic) | 90.30 | 6287.3 | No |
| 757 | 0030-FNXPP | Female | 0 | No | No | 3 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Month-to-month | No | Mailed check | 19.85 | 57.2 | No |
| 2514 | 8922-LIEGH | Female | 1 | No | No | 25 | Yes | Yes | Fiber optic | No | ... | No | No | No | Yes | Month-to-month | Yes | Electronic check | 89.70 | 2187.55 | Yes |
| 4418 | 0378-XSZPU | Male | 0 | Yes | No | 58 | Yes | No | DSL | Yes | ... | Yes | No | No | No | One year | No | Credit card (automatic) | 60.30 | 3563.8 | Yes |
| 6422 | 8668-KNZTI | Male | 0 | No | No | 52 | Yes | No | DSL | Yes | ... | Yes | No | No | No | One year | No | Electronic check | 53.75 | 2790.65 | No |
| 5470 | 6726-NNFWD | Female | 1 | Yes | No | 71 | Yes | No | Fiber optic | No | ... | Yes | No | No | Yes | Two year | No | Credit card (automatic) | 89.45 | 6435.25 | No |
| 677 | 0822-GAVAP | Female | 0 | No | No | 2 | No | No phone service | DSL | No | ... | No | No | No | Yes | Month-to-month | Yes | Electronic check | 34.70 | 62.25 | Yes |
| 1226 | 7115-IRDHS | Female | 0 | Yes | No | 72 | Yes | Yes | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Two year | No | Bank transfer (automatic) | 24.65 | 1830.05 | No |
| 4666 | 8780-RSYYU | Female | 0 | No | No | 25 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | Month-to-month | Yes | Credit card (automatic) | 19.20 | 532.1 | No |
| 3046 | 8715-KKTFG | Female | 0 | Yes | No | 61 | Yes | Yes | Fiber optic | No | ... | Yes | Yes | Yes | Yes | One year | Yes | Bank transfer (automatic) | 103.30 | 6518.35 | No |
| 1412 | 7861-UVUFT | Female | 0 | Yes | No | 15 | Yes | No | Fiber optic | No | ... | Yes | No | Yes | No | Month-to-month | Yes | Electronic check | 84.30 | 1308.4 | Yes |
| 6272 | 4636-OLWOE | Male | 0 | No | Yes | 54 | Yes | No | DSL | No | ... | Yes | Yes | No | No | One year | Yes | Electronic check | 61.00 | 3283.05 | No |
| 4782 | 6175-IRFIT | Male | 0 | No | No | 5 | Yes | No | Fiber optic | No | ... | No | No | No | Yes | Month-to-month | No | Mailed check | 78.75 | 426.35 | No |
| 2262 | 4529-CKBCL | Female | 0 | No | No | 2 | Yes | No | Fiber optic | No | ... | No | No | Yes | No | Month-to-month | Yes | Electronic check | 80.20 | 146.05 | Yes |
| 4371 | 3372-CDXFJ | Male | 0 | Yes | Yes | 13 | Yes | Yes | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | One year | No | Bank transfer (automatic) | 24.50 | 343.6 | No |
| 5194 | 1902-XBTFB | Male | 0 | No | Yes | 22 | Yes | No | Fiber optic | No | ... | Yes | No | Yes | No | Month-to-month | Yes | Electronic check | 89.40 | 2001.5 | Yes |
| 1012 | 7426-RHZGU | Male | 0 | No | No | 9 | Yes | No | Fiber optic | No | ... | No | Yes | Yes | Yes | Month-to-month | Yes | Bank transfer (automatic) | 95.90 | 827.45 | No |
| 174 | 5918-VUKWP | Female | 0 | No | No | 32 | Yes | No | No | No internet service | ... | No internet service | No internet service | No internet service | No internet service | One year | No | Bank transfer (automatic) | 20.55 | 654.55 | No |
50 rows × 21 columns
This is a telecommunications company's customer dataset, containing various demographic and usage information for each customer, as well as whether or not they have churned (i.e. cancelled their service). Here are the meanings of the columns:
# Checking the data types of all the columns
voda.dtypes
customerID object gender object SeniorCitizen int64 Partner object Dependents object tenure int64 PhoneService object MultipleLines object InternetService object OnlineSecurity object OnlineBackup object DeviceProtection object TechSupport object StreamingTV object StreamingMovies object Contract object PaperlessBilling object PaymentMethod object MonthlyCharges float64 TotalCharges object Churn object dtype: object
# Check the descriptive statistics of numeric variables
voda.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| SeniorCitizen | 7043.0 | 0.162147 | 0.368612 | 0.00 | 0.0 | 0.00 | 0.00 | 1.00 |
| tenure | 7043.0 | 32.371149 | 24.559481 | 0.00 | 9.0 | 29.00 | 55.00 | 72.00 |
| MonthlyCharges | 7043.0 | 64.761692 | 30.090047 | 18.25 | 35.5 | 70.35 | 89.85 | 118.75 |
SeniorCitizen is actually a categorical hence the 25%-50%-75% distribution is not propoer
75% customers have tenure less than 55 months
Average Monthly charges are USD 64.76 whereas 25% customers pay more than USD 89.85 per month
voda.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 7043 entries, 0 to 7042 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 customerID 7043 non-null object 1 gender 7043 non-null object 2 SeniorCitizen 7043 non-null int64 3 Partner 7043 non-null object 4 Dependents 7043 non-null object 5 tenure 7043 non-null int64 6 PhoneService 7043 non-null object 7 MultipleLines 7043 non-null object 8 InternetService 7043 non-null object 9 OnlineSecurity 7043 non-null object 10 OnlineBackup 7043 non-null object 11 DeviceProtection 7043 non-null object 12 TechSupport 7043 non-null object 13 StreamingTV 7043 non-null object 14 StreamingMovies 7043 non-null object 15 Contract 7043 non-null object 16 PaperlessBilling 7043 non-null object 17 PaymentMethod 7043 non-null object 18 MonthlyCharges 7043 non-null float64 19 TotalCharges 7043 non-null object 20 Churn 7043 non-null object dtypes: float64(1), int64(2), object(18) memory usage: 1.1+ MB
#Create a copy of the original data
data = voda.copy()
#Typecast TotalCharges column to numeric
data.TotalCharges = pd.to_numeric(data.TotalCharges, errors='coerce')
#Checking number of missing values
data.isnull().sum()
customerID 0 gender 0 SeniorCitizen 0 Partner 0 Dependents 0 tenure 0 PhoneService 0 MultipleLines 0 InternetService 0 OnlineSecurity 0 OnlineBackup 0 DeviceProtection 0 TechSupport 0 StreamingTV 0 StreamingMovies 0 Contract 0 PaperlessBilling 0 PaymentMethod 0 MonthlyCharges 0 TotalCharges 11 Churn 0 dtype: int64
#Fillin with 0s
data['TotalCharges'] = data['TotalCharges'].fillna(0)
# Create new bin labels
labels = ["1-12", "13-24", "25-36","37-48", "49-60", "61-72"]
# Group tenure into new bins and update column
data['tenure_group'] = pd.cut(data.tenure, [1, 13, 25, 37, 49, 61, 73], right=False, labels=labels)
# Count values in new bins and sort by index
tenure_counts = data['tenure_group'].value_counts().sort_index()
#drop column customerID and tenure
data.drop(columns= ['customerID','tenure'], axis=1, inplace=True)
data.head()
| gender | SeniorCitizen | Partner | Dependents | PhoneService | MultipleLines | InternetService | OnlineSecurity | OnlineBackup | DeviceProtection | TechSupport | StreamingTV | StreamingMovies | Contract | PaperlessBilling | PaymentMethod | MonthlyCharges | TotalCharges | Churn | tenure_group | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Female | 0 | Yes | No | No | No phone service | DSL | No | Yes | No | No | No | No | Month-to-month | Yes | Electronic check | 29.85 | 29.85 | No | 1-12 |
| 1 | Male | 0 | No | No | Yes | No | DSL | Yes | No | Yes | No | No | No | One year | No | Mailed check | 56.95 | 1889.50 | No | 25-36 |
| 2 | Male | 0 | No | No | Yes | No | DSL | Yes | Yes | No | No | No | No | Month-to-month | Yes | Mailed check | 53.85 | 108.15 | Yes | 1-12 |
| 3 | Male | 0 | No | No | No | No phone service | DSL | Yes | No | Yes | Yes | No | No | One year | No | Bank transfer (automatic) | 42.30 | 1840.75 | No | 37-48 |
| 4 | Female | 0 | No | No | Yes | No | Fiber optic | No | No | No | No | No | No | Month-to-month | Yes | Electronic check | 70.70 | 151.65 | Yes | 1-12 |
for i, predictor in enumerate(data.drop(columns=['Churn', 'TotalCharges', 'MonthlyCharges'])):
fig = px.histogram(data, x=predictor, color='Churn', barmode='group',
color_discrete_sequence=['#1f77b4', '#aec7e8'],
title=f"Countplot of {predictor} by Churn")
fig.update_layout(xaxis_title=predictor, yaxis_title="Count",
legend_title="Churn", height=400)
fig.show()
Mth_Chgs = sns.kdeplot(data.MonthlyCharges[(data["Churn"] == 'Yes') ],
color="Blue", fill = True, alpha=.5)
Mth_Chgs = sns.kdeplot(data.MonthlyCharges[(data["Churn"] == 'No') ],
ax =Mth_Chgs, color="Green", fill= True, alpha=.5)
Mth_Chgs.legend(["Churn", "No Churn"],loc='upper right')
Mth_Chgs.set_ylabel('Density')
Mth_Chgs.set_xlabel('Monthly Charges')
Mth_Chgs.set_title('Monthly charges by churn')
plt.show()
tot_Chgs = sns.kdeplot(data.TotalCharges[(data["Churn"] == 'No') ],
color="Green", fill=True, alpha=.5)
tot_Chgs = sns.kdeplot(data.TotalCharges[(data["Churn"] == 'Yes') ],
ax =tot_Chgs, color="Blue", fill=True, alpha=.3)
tot_Chgs.legend(["No Churn","Churn"],loc='upper right')
tot_Chgs.set_ylabel('Density')
tot_Chgs.set_xlabel('Total Charges')
tot_Chgs.set_title('Total charges by churn');
fig = px.scatter(data_frame=data, x='MonthlyCharges', y='TotalCharges',
trendline='ols', color='Churn', title='Monthly Charges vs Total Charges')
fig.update_layout(xaxis_title='Monthly Charges', yaxis_title='Total Charges',
margin=dict(l=50, r=50, t=50, b=50), height=400)
fig.show()
Derived Insight:
HIGH Churn seen in case of Month to month contracts, No online security, No Tech support, First year of subscription and Fibre Optics Internet
LOW Churn is seens in case of Long term contracts, Subscriptions without internet service and The customers engaged for 5+ years
Null Hypothesis : Senior citizen does not correlate with the tendency of customer churn.
Alternate Hypothesis : Senior citizen correlate with the tendency of customer churn.
data['Churn'] = np.where(data.Churn == 'Yes',1,0)
from scipy.stats import chi2_contingency
Crosstabresults = pd.crosstab(index= voda['SeniorCitizen'], columns = voda['Churn'])
chisqresult = chi2_contingency(Crosstabresults)
print('p-value: ', chisqresult[1])
p-value: 1.510066805092378e-36
Insights:
P-value is less than 0.05, which implies that we reject our null hypothesis. Senior citizen are more likely to churn.
Null Hypothesis :Gender does not correlate with the tendency of customer churn.
Alternate Hypothesis : Gender citizen correlate with the tendency of customer churn.
Crosstabresults1 = pd.crosstab(index= voda['gender'], columns = voda['Churn'])
chisqresult1 = chi2_contingency(Crosstabresults1)
chisqresult1[1]
0.48657873605618596
Insights:
P-value is greater than 0.05, which implies that we accept our null hypothesis. A customer's tendency to churn soes not depend on their gender.
#Aggregating count of customers over the internet service columns
inter_serv = voda.groupby('InternetService')['customerID'].count().reset_index()
inter_serv.rename(columns={'customerID':'Number'}, inplace =True)
inter_serv
| InternetService | Number | |
|---|---|---|
| 0 | DSL | 2421 |
| 1 | Fiber optic | 3096 |
| 2 | No | 1526 |
data_inter_serv = inter_serv['Number']
keys = inter_serv['InternetService']
colors = px.colors.qualitative.Set1
explode = [0, 0.09, 0]
fig = go.Figure(data=[go.Pie(labels=keys, values=data_inter_serv, pull=[0.1, 0, 0],
textinfo='label+percent', marker=dict(colors=colors))])
fig.update_layout(title='Customer Internet Preference')
fig.show()
#Splitting Data into customer demographic columns
cus_demo = voda.loc[:,'customerID':'Dependents']
cus_demo.rename(columns = {'customerID': 'Number'},inplace = True)
cus_demo
| Number | gender | SeniorCitizen | Partner | Dependents | |
|---|---|---|---|---|---|
| 0 | 7590-VHVEG | Female | 0 | Yes | No |
| 1 | 5575-GNVDE | Male | 0 | No | No |
| 2 | 3668-QPYBK | Male | 0 | No | No |
| 3 | 7795-CFOCW | Male | 0 | No | No |
| 4 | 9237-HQITU | Female | 0 | No | No |
| ... | ... | ... | ... | ... | ... |
| 7038 | 6840-RESVB | Male | 0 | Yes | Yes |
| 7039 | 2234-XADUH | Female | 0 | Yes | Yes |
| 7040 | 4801-JZAZL | Female | 0 | Yes | Yes |
| 7041 | 8361-LTMKD | Male | 1 | Yes | No |
| 7042 | 3186-AJIEK | Male | 0 | No | No |
7043 rows × 5 columns
#Aggregate number of customers over demographic columns: Senior Citizen, Partner and Dependents
cus_demo1 = cus_demo.groupby(['SeniorCitizen','gender'])['Number'].count().reset_index()
cus_demo2 = cus_demo.groupby(['Partner', 'gender'])['Number'].count().reset_index()
cus_demo3 = cus_demo.groupby(['Dependents', 'gender'])['Number'].count().reset_index()
fig1 = px.bar(cus_demo1, x='SeniorCitizen', y='Number', color='gender', barmode='group',
category_orders={'SeniorCitizen': [0, 1]}, color_discrete_sequence=px.colors.qualitative.Pastel1)
fig2 = px.bar(cus_demo2, x='Partner', y='Number', color='gender', barmode='group',
category_orders={'Partner': ['Yes', 'No']}, color_discrete_sequence=px.colors.qualitative.Pastel1)
fig3 = px.bar(cus_demo3, x='Dependents', y='Number', color='gender', barmode='group',
category_orders={'Dependents': ['Yes', 'No']}, color_discrete_sequence=px.colors.qualitative.Pastel1)
fig1.update_layout(title='Customer Demographics', xaxis_title='Senior Citizen', yaxis_title='Number')
fig2.update_layout(xaxis_title='Partner', yaxis_title='Number')
fig3.update_layout(xaxis_title='Dependents', yaxis_title='Number')
fig1.show()
fig2.show()
fig3.show()
Insights:
#slicing vodafone services from the original data
cus_serv = data.loc[:,'PhoneService':'StreamingMovies']
#Aggregating the count over the columns
serv_tally = cus_serv[cus_serv[['PhoneService','MultipleLines','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies']]=='Yes'].count()
serv_tally['InternetService'] = inter_serv['Number'].loc[0:1].sum()
# Sorting in ascending order
serv_tally.sort_values(ascending=True, inplace=True)
# Creating a horizontal bar chart
fig = go.Figure(go.Bar(
x=serv_tally.values,
y=serv_tally.index,
orientation='h',
marker_color='blue' # Setting color of the bars to blue
))
fig.update_layout(title='Most Popular Service', xaxis_title='Number of Customers')
fig.show()
fig = px.box(data_frame=data, x='Dependents', y='MonthlyCharges', color='Dependents',
color_discrete_sequence=['#1f77b4', '#ff7f0e'])
fig.update_layout(title='Monthly Charges by Dependents',
xaxis_title='Dependents', yaxis_title='Monthly Charges')
fig.show()
data.head()
| gender | SeniorCitizen | Partner | Dependents | PhoneService | MultipleLines | InternetService | OnlineSecurity | OnlineBackup | DeviceProtection | TechSupport | StreamingTV | StreamingMovies | Contract | PaperlessBilling | PaymentMethod | MonthlyCharges | TotalCharges | Churn | tenure_group | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Female | 0 | Yes | No | No | No phone service | DSL | No | Yes | No | No | No | No | Month-to-month | Yes | Electronic check | 29.85 | 29.85 | 0 | 1-12 |
| 1 | Male | 0 | No | No | Yes | No | DSL | Yes | No | Yes | No | No | No | One year | No | Mailed check | 56.95 | 1889.50 | 0 | 25-36 |
| 2 | Male | 0 | No | No | Yes | No | DSL | Yes | Yes | No | No | No | No | Month-to-month | Yes | Mailed check | 53.85 | 108.15 | 1 | 1-12 |
| 3 | Male | 0 | No | No | No | No phone service | DSL | Yes | No | Yes | Yes | No | No | One year | No | Bank transfer (automatic) | 42.30 | 1840.75 | 0 | 37-48 |
| 4 | Female | 0 | No | No | Yes | No | Fiber optic | No | No | No | No | No | No | Month-to-month | Yes | Electronic check | 70.70 | 151.65 | 1 | 1-12 |
y = data['Churn']
X = data.drop('Churn', axis =1)
# initialize MinMaxScaler object with specified parameters
scaler = MinMaxScaler(feature_range=(0, 1), copy=True)
# select numerical columns to scale
num_cols = X.select_dtypes(include='number').columns
# scale numerical columns using MinMaxScaler
X[num_cols] = scaler.fit_transform(X[num_cols])
X = pd.get_dummies(X)
y = LabelEncoder().fit_transform(y)
x_train,x_eval,y_train,y_eval=train_test_split(X,y,test_size=0.2)
Approach:
sm = SMOTEENN()
X_resampled, y_resampled = sm.fit_resample(x_train,y_train)
The following models would be used :
tree_clf = DecisionTreeClassifier(criterion = "gini",random_state = 100,max_depth=6, min_samples_leaf=8)
tree_clf.fit(x_train,y_train)
DecisionTreeClassifier(max_depth=6, min_samples_leaf=8, random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
tree_clf_pred= tree_clf.predict(x_eval)
print(classification_report(y_eval, tree_clf_pred, labels=[0,1]))
precision recall f1-score support
0 0.84 0.89 0.87 1038
1 0.64 0.52 0.57 371
accuracy 0.80 1409
macro avg 0.74 0.71 0.72 1409
weighted avg 0.79 0.80 0.79 1409
As you can see that the accuracy is quite low, and as it's an imbalanced dataset, we shouldn't consider Accuracy as our metrics to measure the model..
Hence, we need to check recall, precision & f1 score for the minority class, and it's quite evident that the precision, recall & f1 score is too low for Class 1, i.e. churned customers.
tree_clf_bal = DecisionTreeClassifier(criterion = "gini",random_state = 100,max_depth=6, min_samples_leaf=8)
tree_clf_bal.fit(X_resampled,y_resampled)
tree_bal_pred = tree_clf_bal.predict(x_eval)
print(metrics.classification_report(y_eval,tree_bal_pred))
precision recall f1-score support
0 0.94 0.61 0.74 1038
1 0.45 0.89 0.60 371
accuracy 0.68 1409
macro avg 0.69 0.75 0.67 1409
weighted avg 0.81 0.68 0.70 1409
print(metrics.confusion_matrix(y_eval, tree_bal_pred))
[[632 406] [ 41 330]]
Now we can see quite better results, i.e. Accuracy: 62%, and a very good recall, precision & f1 score for minority class. Let's try with some other classifier.
forest_clf = RandomForestClassifier(n_estimators=100, criterion='gini', random_state = 100,max_depth=6, min_samples_leaf=8)
forest_clf.fit(x_train,y_train)
RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
forest_clf_pred = forest_clf.predict(x_eval)
print(classification_report(y_eval, forest_clf_pred, labels=[0,1]))
precision recall f1-score support
0 0.82 0.92 0.87 1038
1 0.67 0.45 0.54 371
accuracy 0.80 1409
macro avg 0.75 0.68 0.70 1409
weighted avg 0.78 0.80 0.78 1409
Good enough, however lets check with the balance dataset
forest_clf_bal=RandomForestClassifier(n_estimators=100, criterion='gini', random_state = 100,max_depth=6, min_samples_leaf=8)
forest_clf_bal.fit(X_resampled,y_resampled)
RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
forest_bal_pred = forest_clf_bal.predict(x_eval)
print(metrics.classification_report(y_eval, forest_bal_pred))
precision recall f1-score support
0 0.93 0.63 0.75 1038
1 0.45 0.87 0.60 371
accuracy 0.69 1409
macro avg 0.69 0.75 0.67 1409
weighted avg 0.80 0.69 0.71 1409
print(metrics.confusion_matrix(y_eval, forest_bal_pred))
[[650 388] [ 49 322]]
After balancing, the f1 score has marginally improved at the expense of accuracy. This is because prior balancing, the machine was more biased towards the majority class
gb_clf = GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8)
gb_clf.fit(x_train, y_train)
GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
gb_clf_pred = gb_clf.predict(x_eval)
print(classification_report(y_eval, gb_clf_pred, labels=[0,1]))
precision recall f1-score support
0 0.84 0.89 0.86 1038
1 0.63 0.51 0.56 371
accuracy 0.79 1409
macro avg 0.73 0.70 0.71 1409
weighted avg 0.78 0.79 0.79 1409
gb_clf_bal = GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8)
gb_clf_bal.fit(X_resampled,y_resampled)
GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
gb_bal_pred= gb_clf_bal.predict(x_eval)
print(metrics.confusion_matrix(y_eval, gb_bal_pred))
[[727 311] [ 63 308]]
print(metrics.classification_report(y_eval, gb_bal_pred))
precision recall f1-score support
0 0.92 0.70 0.80 1038
1 0.50 0.83 0.62 371
accuracy 0.73 1409
macro avg 0.71 0.77 0.71 1409
weighted avg 0.81 0.73 0.75 1409
Yes, a far better result with the Gradient Boosting Model on the balanced dataset.. we can still check for more classifiers
lr = LogisticRegression(C=1.0, random_state=101)
lr.fit(x_train, y_train)
LogisticRegression(random_state=101)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LogisticRegression(random_state=101)
# Make predictions on the test data using the fitted model
lr_pred = lr.predict(x_eval)
print(classification_report(y_eval, lr_pred, labels=[0,1]))
precision recall f1-score support
0 0.83 0.91 0.87 1038
1 0.66 0.49 0.56 371
accuracy 0.80 1409
macro avg 0.75 0.70 0.72 1409
weighted avg 0.79 0.80 0.79 1409
lr_bal = LogisticRegression(C=1.0, random_state=101)
lr_bal.fit(X_resampled,y_resampled)
LogisticRegression(random_state=101)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LogisticRegression(random_state=101)
lr_bal_pred = lr_bal.predict(x_eval)
print(metrics.confusion_matrix(y_eval, lr_bal_pred))
[[736 302] [ 59 312]]
print(metrics.classification_report(y_eval, lr_bal_pred))
precision recall f1-score support
0 0.93 0.71 0.80 1038
1 0.51 0.84 0.63 371
accuracy 0.74 1409
macro avg 0.72 0.78 0.72 1409
weighted avg 0.82 0.74 0.76 1409
We can see this also is far better: Now that we now the balanced data is better in results.
metrics_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])
models = [
(DecisionTreeClassifier(criterion="gini", random_state=100, max_depth=6, min_samples_leaf=8), 'Decision Tree'),
(RandomForestClassifier(n_estimators=100, criterion='gini', random_state=100, max_depth=6, min_samples_leaf=8), 'Random Forest Classifier'),
(GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8), 'Gradient Boosting Classifier'),
(LogisticRegression(C=1.0, random_state=100), 'Logistic Regression Model')
]
metrics_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])
for model, model_name in models:
model.fit(x_train, y_train)
y_pred = model.predict(x_eval)
accuracy = accuracy_score(y_eval, y_pred)
precision = precision_score(y_eval, y_pred)
recall = recall_score(y_eval, y_pred)
f1 = f1_score(y_eval, y_pred)
metrics_df = metrics_df.append({'Model': model_name, 'Accuracy': accuracy,
'Precision': precision, 'Recall': recall, 'F1 Score': f1}, ignore_index=True)
metrics_df = metrics_df.sort_values(by='F1 Score', ascending=False)
# Print the final metrics dataframe
print(metrics_df)
Model Accuracy Precision Recall F1 Score 0 Decision Tree 0.796309 0.639073 0.520216 0.573551 1 Gradient Boosting Classifier 0.792761 0.632107 0.509434 0.564179 3 Logistic Regression Model 0.799858 0.663004 0.487871 0.562112 2 Random Forest Classifier 0.796309 0.669355 0.447439 0.536349
metrics_df
| Model | Accuracy | Precision | Recall | F1 Score | |
|---|---|---|---|---|---|
| 0 | Decision Tree | 0.796309 | 0.639073 | 0.520216 | 0.573551 |
| 1 | Gradient Boosting Classifier | 0.792761 | 0.632107 | 0.509434 | 0.564179 |
| 3 | Logistic Regression Model | 0.799858 | 0.663004 | 0.487871 | 0.562112 |
| 2 | Random Forest Classifier | 0.796309 | 0.669355 | 0.447439 | 0.536349 |
# Create a list of models and their corresponding parameters
models = [
(DecisionTreeClassifier(criterion="gini", random_state=100, max_depth=6, min_samples_leaf=8), 'Decision Tree'),
(RandomForestClassifier(n_estimators=100, criterion='gini', random_state=100, max_depth=6, min_samples_leaf=8), 'Random Forest Classifier'),
(GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8), 'Gradient Boosting Classifier'),
(LogisticRegression(C=1.0, random_state=100), 'Logistic Regression Model')
]
metrics_bal_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])
sm = SMOTEENN()
X_resampled, y_resampled = sm.fit_resample(x_train, y_train)
for model, model_name in models:
model.fit(X_resampled, y_resampled)
yr_predict = model.predict(x_eval)
accuracy = accuracy_score(y_eval, yr_predict)
precision = precision_score(y_eval, yr_predict)
recall = recall_score(y_eval, yr_predict)
f1 = f1_score(y_eval, yr_predict)
metrics_bal_df = metrics_bal_df.append({'Model': model_name, 'Accuracy': accuracy,
'Precision': precision, 'Recall': recall, 'F1 Score': f1}, ignore_index=True)
# Sort the dataframe in descending order based on Accuracy
metrics_bal_df = metrics_bal_df.sort_values(by='F1 Score', ascending=False)
# Print the final metrics dataframe
print( metrics_bal_df)
Model Accuracy Precision Recall F1 Score 3 Logistic Regression Model 0.743080 0.507293 0.843666 0.633603 2 Gradient Boosting Classifier 0.731725 0.494382 0.830189 0.619718 1 Random Forest Classifier 0.690561 0.454545 0.876011 0.598527 0 Decision Tree 0.678495 0.442577 0.851752 0.582488
metrics_bal_df
| Model | Accuracy | Precision | Recall | F1 Score | |
|---|---|---|---|---|---|
| 3 | Logistic Regression Model | 0.743080 | 0.507293 | 0.843666 | 0.633603 |
| 2 | Gradient Boosting Classifier | 0.731725 | 0.494382 | 0.830189 | 0.619718 |
| 1 | Random Forest Classifier | 0.690561 | 0.454545 | 0.876011 | 0.598527 |
| 0 | Decision Tree | 0.678495 | 0.442577 | 0.851752 | 0.582488 |
From the two tables, it can be observed that:
models = [ gb_clf_bal, lr_bal]
models[0].get_params()
{'ccp_alpha': 0.0,
'criterion': 'friedman_mse',
'init': None,
'learning_rate': 0.1,
'loss': 'log_loss',
'max_depth': 6,
'max_features': None,
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_samples_leaf': 8,
'min_samples_split': 2,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 100,
'n_iter_no_change': None,
'random_state': 100,
'subsample': 1.0,
'tol': 0.0001,
'validation_fraction': 0.1,
'verbose': 0,
'warm_start': False}
gb_clf_params = { 'ccp_alpha': [0.0,0.1,0.2],
'max_depth': [4,6,8],
'min_samples_leaf': [8, 10, 12],
'n_estimators': [100,1000]
}
searcher = GridSearchCV(estimator = gb_clf_bal,
param_grid = gb_clf_params,
scoring = ['accuracy','balanced_accuracy','f1','precision','recall','roc_auc'],
refit = 'balanced_accuracy',
cv = 5,
verbose = 3)
searcher.fit(X_resampled,y_resampled)
Fitting 5 folds for each of 54 candidates, totalling 270 fits [CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.924) balanced_accuracy: (test=0.925) f1: (test=0.933) precision: (test=0.953) recall: (test=0.915) roc_auc: (test=0.980) total time= 1.8s [CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.938) balanced_accuracy: (test=0.936) f1: (test=0.947) precision: (test=0.946) recall: (test=0.948) roc_auc: (test=0.985) total time= 1.7s [CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.972) balanced_accuracy: (test=0.967) f1: (test=0.976) precision: (test=0.958) recall: (test=0.995) roc_auc: (test=0.997) total time= 1.8s [CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.950) balanced_accuracy: (test=0.943) f1: (test=0.958) precision: (test=0.936) recall: (test=0.982) roc_auc: (test=0.993) total time= 1.8s [CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.961) balanced_accuracy: (test=0.957) f1: (test=0.967) precision: (test=0.956) recall: (test=0.978) roc_auc: (test=0.992) total time= 1.8s [CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.918) balanced_accuracy: (test=0.923) f1: (test=0.927) precision: (test=0.966) recall: (test=0.891) roc_auc: (test=0.983) total time= 18.2s [CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.943) balanced_accuracy: (test=0.943) f1: (test=0.951) precision: (test=0.959) recall: (test=0.943) roc_auc: (test=0.986) total time= 17.5s [CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.973) f1: (test=0.981) precision: (test=0.966) recall: (test=0.997) roc_auc: (test=0.998) total time= 17.6s [CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.964) balanced_accuracy: (test=0.957) f1: (test=0.970) precision: (test=0.946) recall: (test=0.995) roc_auc: (test=0.995) total time= 17.5s [CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.974) balanced_accuracy: (test=0.970) f1: (test=0.978) precision: (test=0.966) recall: (test=0.990) roc_auc: (test=0.996) total time= 17.1s [CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.915) balanced_accuracy: (test=0.917) f1: (test=0.925) precision: (test=0.951) recall: (test=0.901) roc_auc: (test=0.980) total time= 1.7s [CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.939) balanced_accuracy: (test=0.938) f1: (test=0.948) precision: (test=0.950) recall: (test=0.946) roc_auc: (test=0.986) total time= 1.7s [CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.973) balanced_accuracy: (test=0.969) f1: (test=0.977) precision: (test=0.963) recall: (test=0.992) roc_auc: (test=0.998) total time= 1.8s [CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.950) balanced_accuracy: (test=0.944) f1: (test=0.958) precision: (test=0.938) recall: (test=0.980) roc_auc: (test=0.993) total time= 2.0s [CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.959) balanced_accuracy: (test=0.955) f1: (test=0.965) precision: (test=0.956) recall: (test=0.975) roc_auc: (test=0.992) total time= 1.8s [CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.913) balanced_accuracy: (test=0.918) f1: (test=0.923) precision: (test=0.962) recall: (test=0.886) roc_auc: (test=0.983) total time= 21.5s [CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.941) balanced_accuracy: (test=0.940) f1: (test=0.949) precision: (test=0.954) recall: (test=0.945) roc_auc: (test=0.988) total time= 17.5s [CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.967) recall: (test=0.997) roc_auc: (test=0.998) total time= 17.7s [CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.963) balanced_accuracy: (test=0.956) f1: (test=0.969) precision: (test=0.944) recall: (test=0.995) roc_auc: (test=0.995) total time= 17.4s [CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.971) balanced_accuracy: (test=0.966) f1: (test=0.975) precision: (test=0.961) recall: (test=0.990) roc_auc: (test=0.996) total time= 17.9s [CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.918) balanced_accuracy: (test=0.919) f1: (test=0.928) precision: (test=0.948) recall: (test=0.910) roc_auc: (test=0.979) total time= 2.0s [CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.937) balanced_accuracy: (test=0.935) f1: (test=0.946) precision: (test=0.945) recall: (test=0.948) roc_auc: (test=0.986) total time= 1.9s [CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.974) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.964) recall: (test=0.993) roc_auc: (test=0.997) total time= 1.9s [CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.950) balanced_accuracy: (test=0.943) f1: (test=0.958) precision: (test=0.935) recall: (test=0.983) roc_auc: (test=0.993) total time= 1.8s [CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.961) balanced_accuracy: (test=0.958) f1: (test=0.967) precision: (test=0.959) recall: (test=0.975) roc_auc: (test=0.993) total time= 1.7s [CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.909) balanced_accuracy: (test=0.915) f1: (test=0.919) precision: (test=0.962) recall: (test=0.880) roc_auc: (test=0.983) total time= 17.4s [CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.938) balanced_accuracy: (test=0.939) f1: (test=0.947) precision: (test=0.957) recall: (test=0.936) roc_auc: (test=0.988) total time= 18.7s [CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.979) balanced_accuracy: (test=0.976) f1: (test=0.983) precision: (test=0.969) recall: (test=0.997) roc_auc: (test=0.998) total time= 17.4s [CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.961) balanced_accuracy: (test=0.954) f1: (test=0.967) precision: (test=0.943) recall: (test=0.993) roc_auc: (test=0.996) total time= 17.4s [CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.971) balanced_accuracy: (test=0.966) f1: (test=0.975) precision: (test=0.960) recall: (test=0.992) roc_auc: (test=0.996) total time= 18.2s [CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.930) balanced_accuracy: (test=0.932) f1: (test=0.939) precision: (test=0.958) recall: (test=0.921) roc_auc: (test=0.986) total time= 2.6s [CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.946) balanced_accuracy: (test=0.945) f1: (test=0.954) precision: (test=0.955) recall: (test=0.953) roc_auc: (test=0.990) total time= 3.4s [CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.967) recall: (test=0.997) roc_auc: (test=0.997) total time= 2.8s [CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.960) balanced_accuracy: (test=0.953) f1: (test=0.967) precision: (test=0.941) recall: (test=0.993) roc_auc: (test=0.995) total time= 2.9s [CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.968) balanced_accuracy: (test=0.964) f1: (test=0.973) precision: (test=0.962) recall: (test=0.983) roc_auc: (test=0.996) total time= 2.9s [CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.926) balanced_accuracy: (test=0.932) f1: (test=0.935) precision: (test=0.971) recall: (test=0.901) roc_auc: (test=0.986) total time= 26.5s [CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.952) f1: (test=0.959) precision: (test=0.966) recall: (test=0.951) roc_auc: (test=0.990) total time= 28.4s [CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.982) balanced_accuracy: (test=0.979) f1: (test=0.985) precision: (test=0.971) recall: (test=1.000) roc_auc: (test=0.998) total time= 27.5s [CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.967) balanced_accuracy: (test=0.961) f1: (test=0.972) precision: (test=0.950) recall: (test=0.995) roc_auc: (test=0.996) total time= 26.1s [CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.968) recall: (test=0.997) roc_auc: (test=0.997) total time= 26.2s [CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.932) balanced_accuracy: (test=0.935) f1: (test=0.941) precision: (test=0.963) recall: (test=0.920) roc_auc: (test=0.984) total time= 2.5s [CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.942) balanced_accuracy: (test=0.941) f1: (test=0.950) precision: (test=0.953) recall: (test=0.948) roc_auc: (test=0.990) total time= 2.9s [CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.981) balanced_accuracy: (test=0.978) f1: (test=0.984) precision: (test=0.971) recall: (test=0.998) roc_auc: (test=0.998) total time= 3.0s [CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.958) balanced_accuracy: (test=0.951) f1: (test=0.965) precision: (test=0.940) recall: (test=0.992) roc_auc: (test=0.995) total time= 2.5s [CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.971) balanced_accuracy: (test=0.968) f1: (test=0.975) precision: (test=0.966) recall: (test=0.985) roc_auc: (test=0.996) total time= 2.5s [CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.925) balanced_accuracy: (test=0.931) f1: (test=0.934) precision: (test=0.969) recall: (test=0.901) roc_auc: (test=0.986) total time= 27.5s [CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.950) balanced_accuracy: (test=0.950) f1: (test=0.957) precision: (test=0.966) recall: (test=0.948) roc_auc: (test=0.989) total time= 27.1s [CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.984) balanced_accuracy: (test=0.981) f1: (test=0.987) precision: (test=0.974) recall: (test=1.000) roc_auc: (test=0.998) total time= 26.3s [CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.967) balanced_accuracy: (test=0.961) f1: (test=0.972) precision: (test=0.950) recall: (test=0.995) roc_auc: (test=0.995) total time= 26.2s [CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.975) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.964) recall: (test=0.995) roc_auc: (test=0.996) total time= 27.0s [CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.930) balanced_accuracy: (test=0.934) f1: (test=0.939) precision: (test=0.966) recall: (test=0.913) roc_auc: (test=0.985) total time= 3.0s [CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.947) balanced_accuracy: (test=0.946) f1: (test=0.955) precision: (test=0.959) recall: (test=0.950) roc_auc: (test=0.990) total time= 2.8s [CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.980) balanced_accuracy: (test=0.977) f1: (test=0.983) precision: (test=0.969) recall: (test=0.998) roc_auc: (test=0.998) total time= 2.7s [CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.960) balanced_accuracy: (test=0.953) f1: (test=0.967) precision: (test=0.943) recall: (test=0.992) roc_auc: (test=0.995) total time= 2.7s [CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.966) balanced_accuracy: (test=0.962) f1: (test=0.971) precision: (test=0.961) recall: (test=0.982) roc_auc: (test=0.995) total time= 2.5s [CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.926) balanced_accuracy: (test=0.931) f1: (test=0.935) precision: (test=0.968) recall: (test=0.905) roc_auc: (test=0.985) total time= 25.7s [CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.948) balanced_accuracy: (test=0.948) f1: (test=0.955) precision: (test=0.963) recall: (test=0.948) roc_auc: (test=0.989) total time= 27.3s [CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.983) balanced_accuracy: (test=0.980) f1: (test=0.986) precision: (test=0.972) recall: (test=1.000) roc_auc: (test=0.998) total time= 26.0s [CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.969) balanced_accuracy: (test=0.963) f1: (test=0.974) precision: (test=0.952) recall: (test=0.997) roc_auc: (test=0.996) total time= 27.0s [CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.974) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.966) recall: (test=0.992) roc_auc: (test=0.996) total time= 25.6s [CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.938) balanced_accuracy: (test=0.941) f1: (test=0.946) precision: (test=0.968) recall: (test=0.925) roc_auc: (test=0.987) total time= 3.8s [CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.953) balanced_accuracy: (test=0.952) f1: (test=0.960) precision: (test=0.963) recall: (test=0.956) roc_auc: (test=0.991) total time= 4.1s [CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.975) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.963) recall: (test=0.997) roc_auc: (test=0.998) total time= 3.5s [CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.962) balanced_accuracy: (test=0.955) f1: (test=0.968) precision: (test=0.943) recall: (test=0.995) roc_auc: (test=0.996) total time= 3.5s [CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.974) balanced_accuracy: (test=0.971) f1: (test=0.978) precision: (test=0.970) recall: (test=0.985) roc_auc: (test=0.995) total time= 3.7s [CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.939) balanced_accuracy: (test=0.943) f1: (test=0.947) precision: (test=0.972) recall: (test=0.923) roc_auc: (test=0.987) total time= 30.8s [CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.952) f1: (test=0.960) precision: (test=0.963) recall: (test=0.956) roc_auc: (test=0.991) total time= 28.1s [CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.983) balanced_accuracy: (test=0.980) f1: (test=0.986) precision: (test=0.972) recall: (test=1.000) roc_auc: (test=0.999) total time= 28.7s [CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.966) balanced_accuracy: (test=0.960) f1: (test=0.971) precision: (test=0.949) recall: (test=0.995) roc_auc: (test=0.996) total time= 30.3s [CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.967) recall: (test=0.995) roc_auc: (test=0.996) total time= 28.2s [CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.936) balanced_accuracy: (test=0.938) f1: (test=0.945) precision: (test=0.963) recall: (test=0.926) roc_auc: (test=0.988) total time= 3.4s [CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.955) balanced_accuracy: (test=0.954) f1: (test=0.961) precision: (test=0.963) recall: (test=0.960) roc_auc: (test=0.991) total time= 3.4s [CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.980) balanced_accuracy: (test=0.977) f1: (test=0.983) precision: (test=0.971) recall: (test=0.997) roc_auc: (test=0.998) total time= 3.7s [CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.963) balanced_accuracy: (test=0.956) f1: (test=0.969) precision: (test=0.946) recall: (test=0.993) roc_auc: (test=0.996) total time= 3.7s [CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.971) balanced_accuracy: (test=0.968) f1: (test=0.975) precision: (test=0.966) recall: (test=0.985) roc_auc: (test=0.996) total time= 3.4s [CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.932) balanced_accuracy: (test=0.936) f1: (test=0.941) precision: (test=0.970) recall: (test=0.913) roc_auc: (test=0.986) total time= 34.2s [CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.955) balanced_accuracy: (test=0.954) f1: (test=0.961) precision: (test=0.965) recall: (test=0.958) roc_auc: (test=0.990) total time= 34.1s [CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.982) balanced_accuracy: (test=0.979) f1: (test=0.985) precision: (test=0.971) recall: (test=1.000) roc_auc: (test=0.998) total time= 33.7s [CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.967) balanced_accuracy: (test=0.961) f1: (test=0.972) precision: (test=0.953) recall: (test=0.992) roc_auc: (test=0.995) total time= 34.1s [CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.973) balanced_accuracy: (test=0.968) f1: (test=0.977) precision: (test=0.961) recall: (test=0.993) roc_auc: (test=0.996) total time= 34.4s [CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.942) balanced_accuracy: (test=0.944) f1: (test=0.950) precision: (test=0.969) recall: (test=0.931) roc_auc: (test=0.988) total time= 3.6s [CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.951) balanced_accuracy: (test=0.950) f1: (test=0.958) precision: (test=0.961) recall: (test=0.955) roc_auc: (test=0.991) total time= 3.5s [CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.982) balanced_accuracy: (test=0.979) f1: (test=0.985) precision: (test=0.972) recall: (test=0.998) roc_auc: (test=0.998) total time= 3.3s [CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.963) balanced_accuracy: (test=0.956) f1: (test=0.969) precision: (test=0.946) recall: (test=0.993) roc_auc: (test=0.996) total time= 3.6s [CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.973) balanced_accuracy: (test=0.969) f1: (test=0.977) precision: (test=0.966) recall: (test=0.988) roc_auc: (test=0.996) total time= 3.3s [CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.925) balanced_accuracy: (test=0.931) f1: (test=0.934) precision: (test=0.969) recall: (test=0.901) roc_auc: (test=0.986) total time= 33.2s [CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.951) f1: (test=0.960) precision: (test=0.960) recall: (test=0.960) roc_auc: (test=0.990) total time= 33.9s [CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.980) balanced_accuracy: (test=0.976) f1: (test=0.984) precision: (test=0.968) recall: (test=1.000) roc_auc: (test=0.998) total time= 34.6s [CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.969) balanced_accuracy: (test=0.963) f1: (test=0.974) precision: (test=0.955) recall: (test=0.993) roc_auc: (test=0.996) total time= 33.8s [CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.975) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.964) recall: (test=0.995) roc_auc: (test=0.996) total time= 33.5s [CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 1.7s [CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 1.8s [CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 1.5s [CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 1.5s [CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 1.5s [CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 16.8s [CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 16.7s [CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 16.6s [CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 16.2s [CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 16.8s [CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 1.6s [CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 1.5s [CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 1.6s [CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.0s [CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 1.6s [CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 16.7s [CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 16.8s [CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 16.9s [CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 16.1s [CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 16.2s [CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 1.5s [CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 1.6s [CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 1.6s [CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 1.5s [CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 1.5s [CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 16.7s [CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 17.2s [CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 15.9s [CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 16.7s [CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 16.8s [CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.3s [CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.2s [CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 2.3s [CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.4s [CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 2.5s [CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 23.3s [CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 23.0s [CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 23.5s [CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 22.9s [CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 24.0s [CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.2s [CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.2s [CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 2.3s [CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.1s [CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 2.1s [CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 23.9s [CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 23.0s [CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 22.6s [CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 24.9s [CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 23.6s [CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.1s [CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.2s [CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 2.3s [CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.2s [CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 2.4s [CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 22.2s [CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 23.5s [CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 22.7s [CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 23.8s [CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 22.9s [CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.6s [CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.6s [CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 2.7s [CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.6s [CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 3.0s [CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 26.4s [CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 32.0s [CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 30.0s [CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 29.1s [CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 28.2s [CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.6s [CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.6s [CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 2.6s [CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.6s [CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 3.0s [CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 26.6s [CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 28.9s [CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 28.7s [CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 28.0s [CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 27.5s [CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 3.0s [CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 2.6s [CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 3.1s [CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 2.8s [CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 2.8s [CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 27.4s [CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time= 35.6s [CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time= 28.7s [CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time= 29.1s [CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time= 28.2s [CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.6s [CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.6s [CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.6s [CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.8s [CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.6s [CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.6s [CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 17.3s [CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 18.2s [CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.5s [CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.6s [CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.6s [CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.7s [CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.5s [CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.5s [CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.7s [CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.7s [CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.4s [CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 18.3s [CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.0s [CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 17.3s [CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.0s [CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.6s [CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.8s [CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.7s [CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 1.5s [CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 17.8s [CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.6s [CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 15.9s [CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 15.8s [CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 16.3s [CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.2s [CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.3s [CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.5s [CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.1s [CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.1s [CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.3s [CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.4s [CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 24.3s [CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 22.9s [CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.3s [CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.2s [CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.7s [CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.1s [CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.3s [CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 24.2s [CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.0s [CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.1s [CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 24.1s [CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.9s [CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.7s [CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.4s [CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.2s [CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.2s [CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 24.5s [CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 23.1s [CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 22.6s [CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 22.3s [CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 22.2s [CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 3.0s [CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.7s [CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.8s [CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.8s [CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 28.8s [CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.3s [CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 28.7s [CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.8s [CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 28.5s [CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.8s [CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.7s [CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 3.0s [CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 28.7s [CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 26.9s [CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.7s [CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 29.3s [CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.0s [CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.9s [CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.6s [CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 3.0s [CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 2.7s [CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.6s [CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.4s [CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.4s [CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time= 28.1s [CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time= 27.9s
GridSearchCV(cv=5,
estimator=GradientBoostingClassifier(max_depth=6,
min_samples_leaf=8,
random_state=100),
param_grid={'ccp_alpha': [0.0, 0.1, 0.2], 'max_depth': [4, 6, 8],
'min_samples_leaf': [8, 10, 12],
'n_estimators': [100, 1000]},
refit='balanced_accuracy',
scoring=['accuracy', 'balanced_accuracy', 'f1', 'precision',
'recall', 'roc_auc'],
verbose=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,
estimator=GradientBoostingClassifier(max_depth=6,
min_samples_leaf=8,
random_state=100),
param_grid={'ccp_alpha': [0.0, 0.1, 0.2], 'max_depth': [4, 6, 8],
'min_samples_leaf': [8, 10, 12],
'n_estimators': [100, 1000]},
refit='balanced_accuracy',
scoring=['accuracy', 'balanced_accuracy', 'f1', 'precision',
'recall', 'roc_auc'],
verbose=3)GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)
gb_clf_tuned = searcher.best_estimator_
gb_clf_tuned
GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
random_state=100)models.append(gb_clf_tuned)
models
[GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100),
LogisticRegression(random_state=101),
GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
random_state=100)]
models[1].get_params()
{'C': 1.0,
'class_weight': None,
'dual': False,
'fit_intercept': True,
'intercept_scaling': 1,
'l1_ratio': None,
'max_iter': 100,
'multi_class': 'auto',
'n_jobs': None,
'penalty': 'l2',
'random_state': 101,
'solver': 'lbfgs',
'tol': 0.0001,
'verbose': 0,
'warm_start': False}
lr_params = { 'C': [1.0,2.0,3.0],
'max_iter': [1000,10000,100000],
'intercept_scaling': [1, 2, 3,]
}
searcher_lr = GridSearchCV(estimator = lr_bal,
param_grid = lr_params,
scoring = ['accuracy','balanced_accuracy','f1','precision','recall','roc_auc'],
refit = 'balanced_accuracy',
cv = 5,
verbose = 3
)
searcher_lr.fit(X_resampled, y_resampled)
Fitting 5 folds for each of 27 candidates, totalling 135 fits [CV 1/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.2s [CV 2/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.1s [CV 4/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.0s [CV 5/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.1s [CV 1/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.0s [CV 2/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.1s [CV 4/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.0s [CV 5/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.0s [CV 1/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.0s [CV 2/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.1s [CV 4/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.0s [CV 5/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.0s [CV 1/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.1s [CV 2/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.1s [CV 4/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.0s [CV 5/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.0s [CV 1/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.0s [CV 2/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.1s [CV 4/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.1s [CV 5/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.1s [CV 1/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.1s [CV 2/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.2s [CV 4/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.1s [CV 5/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.1s [CV 1/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.1s [CV 2/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.2s [CV 4/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.1s [CV 5/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.1s [CV 1/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.1s [CV 2/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.1s [CV 4/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.0s [CV 5/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.0s [CV 1/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time= 0.1s [CV 2/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time= 0.2s [CV 4/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time= 0.0s [CV 5/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time= 0.0s [CV 1/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.0s [CV 5/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.2s [CV 3/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.2s [CV 2/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.0s [CV 5/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.0s [CV 4/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time= 0.0s [CV 2/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.0s [CV 3/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time= 0.1s [CV 5/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.2s [CV 4/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.2s [CV 1/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.2s [CV 2/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.2s [CV 3/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.2s [CV 4/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.2s [CV 5/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.2s [CV 1/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.3s [CV 2/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.2s [CV 3/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s [CV 1/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time= 0.1s [CV 2/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time= 0.1s [CV 3/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time= 0.1s [CV 4/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time= 0.1s [CV 5/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time= 0.1s
GridSearchCV(cv=5, estimator=LogisticRegression(random_state=101),
param_grid={'C': [1.0, 2.0, 3.0], 'intercept_scaling': [1, 2, 3],
'max_iter': [1000, 10000, 100000]},
refit='balanced_accuracy',
scoring=['accuracy', 'balanced_accuracy', 'f1', 'precision',
'recall', 'roc_auc'],
verbose=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5, estimator=LogisticRegression(random_state=101),
param_grid={'C': [1.0, 2.0, 3.0], 'intercept_scaling': [1, 2, 3],
'max_iter': [1000, 10000, 100000]},
refit='balanced_accuracy',
scoring=['accuracy', 'balanced_accuracy', 'f1', 'precision',
'recall', 'roc_auc'],
verbose=3)LogisticRegression(random_state=101)
LogisticRegression(random_state=101)
lr_tuned = searcher_lr.best_estimator_
models.append(lr_tuned)
models
[GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100),
LogisticRegression(random_state=101),
GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
random_state=100),
LogisticRegression(C=3.0, max_iter=1000, random_state=101)]
components = { 'scaler': scaler,
'model': models
}
components
{'scaler': MinMaxScaler(),
'model': [GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100),
LogisticRegression(random_state=101),
GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
random_state=100),
LogisticRegression(C=3.0, max_iter=1000, random_state=101)]}
#Create Folder
!mkdir export
#Create a destination folder
destination = os.path.join('.','export')
#export
with open(os.path.join(destination,'ml.pkl'),'wb') as f:
pickle.dump(components, f)
#requirements
!pip freeze requirement.txt
alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work
WARNING: Ignoring invalid distribution -tatsmodels (c:\users\acer\anaconda3\lib\site-packages)
anaconda-client @ file:///C:/ci/anaconda-client_1635342725944/work anaconda-navigator==2.3.2 anaconda-project @ file:///tmp/build/80754af9/anaconda-project_1626085644852/work anyio @ file:///C:/ci/anyio_1620153135622/work/dist appdirs==1.4.4 argh==0.26.2 argon2-cffi @ file:///C:/ci/argon2-cffi_1613037869401/work arrow @ file:///C:/ci/arrow_1617738834352/work asn1crypto @ file:///tmp/build/80754af9/asn1crypto_1596577642040/work astroid @ file:///C:/ci/astroid_1628063282661/work astropy @ file:///C:/ci/astropy_1629829318700/work async-generator @ file:///home/ktietz/src/ci/async_generator_1611927993394/work atomicwrites==1.4.0 attrs @ file:///tmp/build/80754af9/attrs_1620827162558/work Automat @ file:///tmp/build/80754af9/automat_1600298431173/work autopep8 @ file:///tmp/build/80754af9/autopep8_1620866417880/work Babel @ file:///tmp/build/80754af9/babel_1620871417480/work backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work backports.functools-lru-cache @ file:///tmp/build/80754af9/backports.functools_lru_cache_1618170165463/work backports.shutil-get-terminal-size @ file:///tmp/build/80754af9/backports.shutil_get_terminal_size_1608222128777/work backports.tempfile @ file:///home/linux1/recipes/ci/backports.tempfile_1610991236607/work backports.weakref==1.0.post1 bcrypt @ file:///C:/ci/bcrypt_1607022693089/work beautifulsoup4 @ file:///tmp/build/80754af9/beautifulsoup4_1631874778482/work binaryornot @ file:///tmp/build/80754af9/binaryornot_1617751525010/work bitarray @ file:///C:/ci/bitarray_1629133068652/work bkcharts==0.2 black==19.10b0 bleach @ file:///tmp/build/80754af9/bleach_1628110601003/work bokeh @ file:///C:/ci/bokeh_1635306491714/work boto==2.49.0 boto3 @ file:///C:/Windows/TEMP/abs_4009c406-44ba-4406-8996-204d9b11202flt4kglbk/croots/recipe/boto3_1657820114895/work botocore @ file:///C:/b/abs_807k4c4xt1/croot/botocore_1667495381641/work Bottleneck @ file:///C:/ci/bottleneck_1607557040328/work brotlipy==0.7.0 cached-property @ file:///tmp/build/80754af9/cached-property_1600785575025/work category-encoders==2.6.0 certifi @ file:///C:/b/abs_4a0polqwty/croot/certifi_1683875377622/work/certifi cffi @ file:///C:/ci/cffi_1625831756778/work chardet @ file:///C:/ci/chardet_1607706937985/work charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work click==8.0.3 cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1632508026186/work clyent==1.2.2 colorama @ file:///tmp/build/80754af9/colorama_1607707115595/work colorcet @ file:///C:/b/abs_46vyu0rpdl/croot/colorcet_1668084513237/work comtypes==1.1.10 conda==4.12.0 conda-build==3.21.6 conda-content-trust @ file:///tmp/build/80754af9/conda-content-trust_1617045594566/work conda-pack @ file:///tmp/build/80754af9/conda-pack_1611163042455/work conda-package-handling @ file:///C:/ci/conda-package-handling_1618262410900/work conda-repo-cli @ file:///tmp/build/80754af9/conda-repo-cli_1620168426516/work conda-token @ file:///tmp/build/80754af9/conda-token_1620076980546/work conda-verify==3.4.2 constantly==15.1.0 contextlib2 @ file:///Users/ktietz/demo/mc3/conda-bld/contextlib2_1630668244042/work conti==1.0 cookiecutter @ file:///tmp/build/80754af9/cookiecutter_1617748928239/work cryptography @ file:///C:/ci/cryptography_1633520531101/work cssselect==1.1.0 cycler==0.10.0 Cython @ file:///C:/ci/cython_1636018292912/work cytoolz==0.11.0 daal4py==2021.3.0 dask==2021.10.0 datashader @ file:///C:/b/abs_cdf2s6zvt6/croot/datashader_1670841914509/work datashape==0.5.4 debugpy @ file:///C:/ci/debugpy_1629222819322/work decorator @ file:///tmp/build/80754af9/decorator_1632776554403/work defusedxml @ file:///tmp/build/80754af9/defusedxml_1615228127516/work diff-match-patch @ file:///Users/ktietz/demo/mc3/conda-bld/diff-match-patch_1630511840874/work distributed @ file:///C:/ci/distributed_1635968318313/work docutils @ file:///C:/ci/docutils_1620828264669/work emoji==2.2.0 entrypoints==0.3 et-xmlfile==1.1.0 Faker==16.6.1 fastcache @ file:///C:/ci/fastcache_1607571310570/work filelock @ file:///tmp/build/80754af9/filelock_1635402558181/work flake8 @ file:///tmp/build/80754af9/flake8_1620776156532/work Flask @ file:///home/ktietz/src/ci/flask_1611932660458/work fonttools==4.25.0 fsspec @ file:///tmp/build/80754af9/fsspec_1636116461911/work future @ file:///C:/ci/future_1607568713721/work gensim @ file:///C:/ci/gensim_1613994497440/work gevent @ file:///C:/ci/gevent_1628273776273/work glob2 @ file:///home/linux1/recipes/ci/glob2_1610991677669/work greenlet @ file:///C:/ci/greenlet_1628888275363/work h5py @ file:///C:/ci/h5py_1622088609188/work HeapDict @ file:///Users/ktietz/demo/mc3/conda-bld/heapdict_1630598515714/work holoviews @ file:///opt/conda/conda-bld/holoviews_1645454331194/work html5lib @ file:///Users/ktietz/demo/mc3/conda-bld/html5lib_1629144453894/work hvplot @ file:///C:/b/abs_13un17_4x_/croot/hvplot_1670508919193/work hyperlink @ file:///tmp/build/80754af9/hyperlink_1610130746837/work idna @ file:///tmp/build/80754af9/idna_1622654382723/work imagecodecs @ file:///C:/ci/imagecodecs_1635511087451/work imageio @ file:///tmp/build/80754af9/imageio_1617700267927/work imagesize @ file:///Users/ktietz/demo/mc3/conda-bld/imagesize_1628863108022/work imbalanced-learn==0.10.1 imblearn==0.0 importlib-metadata @ file:///C:/ci/importlib-metadata_1631916826748/work importlib-resources==5.10.2 incremental @ file:///tmp/build/80754af9/incremental_1636629750599/work inflection==0.5.1 iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work intake @ file:///C:/b/abs_56qe6jc56w/croot/intake_1668765380923/work intervaltree @ file:///Users/ktietz/demo/mc3/conda-bld/intervaltree_1630511889664/work ipykernel @ file:///C:/ci/ipykernel_1633545585502/work/dist/ipykernel-6.4.1-py3-none-any.whl ipython @ file:///C:/ci/ipython_1635944283918/work ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work ipywidgets @ file:///tmp/build/80754af9/ipywidgets_1634143127070/work isort @ file:///tmp/build/80754af9/isort_1628603791788/work itemadapter @ file:///tmp/build/80754af9/itemadapter_1626442940632/work itemloaders @ file:///opt/conda/conda-bld/itemloaders_1646805235997/work itsdangerous @ file:///tmp/build/80754af9/itsdangerous_1621432558163/work jdcal @ file:///Users/ktietz/demo/mc3/conda-bld/jdcal_1630584345063/work jedi @ file:///C:/ci/jedi_1611341083684/work Jinja2 @ file:///tmp/build/80754af9/jinja2_1612213139570/work jinja2-time @ file:///tmp/build/80754af9/jinja2-time_1617751524098/work jmespath @ file:///Users/ktietz/demo/mc3/conda-bld/jmespath_1630583964805/work joblib==1.2.0 json5 @ file:///tmp/build/80754af9/json5_1624432770122/work jsonschema @ file:///Users/ktietz/demo/mc3/conda-bld/jsonschema_1630511932244/work jupyter @ file:///C:/ci/jupyter_1607685287094/work jupyter-client @ file:///tmp/build/80754af9/jupyter_client_1616770841739/work jupyter-console @ file:///tmp/build/80754af9/jupyter_console_1616615302928/work jupyter-core @ file:///C:/ci/jupyter_core_1633420716440/work jupyter-server @ file:///C:/ci/jupyter_server_1616084298419/work jupyterlab @ file:///tmp/build/80754af9/jupyterlab_1635799997693/work jupyterlab-pygments @ file:///tmp/build/80754af9/jupyterlab_pygments_1601490720602/work jupyterlab-server @ file:///tmp/build/80754af9/jupyterlab_server_1633419203660/work jupyterlab-widgets @ file:///tmp/build/80754af9/jupyterlab_widgets_1609884341231/work keyring @ file:///C:/ci/keyring_1629321702436/work kiwisolver @ file:///C:/ci/kiwisolver_1612282555033/work lazy-object-proxy @ file:///C:/ci/lazy-object-proxy_1616529288960/work libarchive-c @ file:///tmp/build/80754af9/python-libarchive-c_1617780486945/work llvmlite==0.39.1 locket==0.2.1 lxml @ file:///C:/ci/lxml_1616443418777/work Markdown @ file:///C:/b/abs_98lv_ucina/croot/markdown_1671541919225/work MarkupSafe @ file:///C:/ci/markupsafe_1607027406824/work matplotlib @ file:///C:/ci/matplotlib-suite_1634667159685/work matplotlib-inline @ file:///tmp/build/80754af9/matplotlib-inline_1628242447089/work mccabe==0.6.1 menuinst @ file:///C:/ci/menuinst_1631733438520/work mistune @ file:///C:/ci/mistune_1607359457024/work mkl-fft==1.3.1 mkl-random @ file:///C:/ci/mkl_random_1626186184308/work mkl-service==2.4.0 mock @ file:///tmp/build/80754af9/mock_1607622725907/work more-itertools @ file:///tmp/build/80754af9/more-itertools_1635423142362/work mpmath==1.2.1 msgpack @ file:///C:/ci/msgpack-python_1612287350784/work multipledispatch @ file:///C:/ci/multipledispatch_1607574329826/work munkres==1.1.4 mypy-extensions==0.4.3 navigator-updater==0.2.1 nbclassic @ file:///tmp/build/80754af9/nbclassic_1616085367084/work nbclient @ file:///tmp/build/80754af9/nbclient_1614364831625/work nbconvert @ file:///C:/ci/nbconvert_1624479160025/work nbformat @ file:///tmp/build/80754af9/nbformat_1617383369282/work nest-asyncio @ file:///tmp/build/80754af9/nest-asyncio_1613680548246/work networkx @ file:///tmp/build/80754af9/networkx_1633639043937/work nltk==3.6.5 nose @ file:///tmp/build/80754af9/nose_1606773131901/work notebook @ file:///C:/ci/notebook_1635393701545/work numba @ file:///C:/b/abs_e53pp2e4k7/croot/numba_1670258349527/work numexpr @ file:///C:/ci/numexpr_1618856728739/work numpy @ file:///C:/b/abs_datssh7cer/croot/numpy_and_numpy_base_1672336199388/work numpydoc @ file:///tmp/build/80754af9/numpydoc_1605117425582/work nxtools==1.6 olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work openpyxl @ file:///tmp/build/80754af9/openpyxl_1632777717936/work packaging==23.1 pandas @ file:///C:/b/abs_cdcgk91igc/croots/recipe/pandas_1663772960432/work pandocfilters @ file:///C:/ci/pandocfilters_1605114832805/work panel @ file:///C:/b/abs_37xf16wbwu/croot/panel_1674166324937/work param @ file:///C:/b/abs_d799n8xz_7/croot/param_1671697759755/work paramiko @ file:///tmp/build/80754af9/paramiko_1598886428689/work parsel @ file:///C:/ci/parsel_1646740216444/work parso @ file:///tmp/build/80754af9/parso_1617223946239/work partd @ file:///tmp/build/80754af9/partd_1618000087440/work path @ file:///C:/ci/path_1624287837534/work pathlib2 @ file:///C:/ci/pathlib2_1625585796814/work pathspec==0.7.0 patsy==0.5.2 pep8==1.7.1 pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work Pillow==8.4.0 pkginfo==1.7.1 plotly @ file:///C:/ci/plotly_1658142442431/work pluggy @ file:///C:/ci/pluggy_1615976440052/work ply==3.11 pmdarima==2.0.3 poyo @ file:///tmp/build/80754af9/poyo_1617751526755/work prometheus-client @ file:///tmp/build/80754af9/prometheus_client_1623189609245/work prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1633440160888/work Protego @ file:///tmp/build/80754af9/protego_1598657180827/work psutil @ file:///C:/ci/psutil_1612298199233/work ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl py @ file:///tmp/build/80754af9/py_1607971587848/work pyasn1 @ file:///Users/ktietz/demo/mc3/conda-bld/pyasn1_1629708007385/work pyasn1-modules==0.2.8 pycodestyle @ file:///tmp/build/80754af9/pycodestyle_1615748559966/work pycosat==0.6.3 pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work pyct @ file:///C:/ci/pyct_1658488033428/work pycurl==7.44.1 PyDispatcher==2.0.5 pydocstyle @ file:///tmp/build/80754af9/pydocstyle_1621600989141/work pyerfa @ file:///C:/ci/pyerfa_1621560974055/work pyflakes @ file:///tmp/build/80754af9/pyflakes_1617200973297/work Pygments @ file:///tmp/build/80754af9/pygments_1629234116488/work PyHamcrest @ file:///tmp/build/80754af9/pyhamcrest_1615748656804/work PyJWT @ file:///C:/ci/pyjwt_1619682721924/work pylint @ file:///C:/ci/pylint_1627536884966/work pyls-spyder==0.4.0 PyNaCl @ file:///C:/ci/pynacl_1607612759007/work pyodbc===4.0.0-unsupported pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1635333100036/work pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work pyreadline==2.1 pyrsistent @ file:///C:/ci/pyrsistent_1636093225342/work PySocks @ file:///C:/ci/pysocks_1605307512533/work pytest==6.2.4 python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work python-lsp-black @ file:///tmp/build/80754af9/python-lsp-black_1634232156041/work python-lsp-jsonrpc==1.0.0 python-lsp-server==1.2.4 python-slugify @ file:///tmp/build/80754af9/python-slugify_1620405669636/work python-snappy @ file:///C:/ci/python-snappy_1610133405910/work pytz==2021.3 pyviz-comms @ file:///tmp/build/80754af9/pyviz_comms_1623747165329/work PyWavelets @ file:///C:/ci/pywavelets_1607645631519/work pywin32==228 pywin32-ctypes @ file:///C:/ci/pywin32-ctypes_1607553594546/work pywinpty @ file:///C:/ci/pywinpty_1607419945780/work PyYAML==6.0 pyzmq @ file:///C:/ci/pyzmq_1628276105793/work QDarkStyle @ file:///tmp/build/80754af9/qdarkstyle_1617386714626/work qstylizer @ file:///tmp/build/80754af9/qstylizer_1617713584600/work/dist/qstylizer-0.1.10-py2.py3-none-any.whl QtAwesome @ file:///tmp/build/80754af9/qtawesome_1615991616277/work qtconsole @ file:///tmp/build/80754af9/qtconsole_1632739723211/work QtPy @ file:///tmp/build/80754af9/qtpy_1629397026935/work queuelib==1.5.0 regex @ file:///C:/ci/regex_1628063427816/work requests @ file:///tmp/build/80754af9/requests_1629994808627/work requests-file @ file:///Users/ktietz/demo/mc3/conda-bld/requests-file_1629455781986/work rise @ file:///C:/ci/rise_1612306697775/work rope @ file:///tmp/build/80754af9/rope_1623703006312/work Rtree @ file:///C:/ci/rtree_1618421015405/work ruamel-yaml-conda @ file:///C:/ci/ruamel_yaml_1616016898638/work s3transfer @ file:///C:/ci/s3transfer_1654512518418/work scikit-image==0.18.3 scikit-learn==1.2.1 scikit-learn-intelex==2021.20210714.120553 scipy @ file:///C:/ci/scipy_1630606917240/work Scrapy @ file:///C:/ci/scrapy_1646837986255/work seaborn @ file:///tmp/build/80754af9/seaborn_1629307859561/work Send2Trash @ file:///tmp/build/80754af9/send2trash_1632406701022/work service-identity @ file:///Users/ktietz/demo/mc3/conda-bld/service_identity_1629460757137/work simplegeneric==0.8.1 singledispatch @ file:///tmp/build/80754af9/singledispatch_1629321204894/work sip==4.19.13 six @ file:///tmp/build/80754af9/six_1623709665295/work smart-open @ file:///C:/ci/smart_open_1651235069716/work sniffio @ file:///C:/ci/sniffio_1614030527509/work snowballstemmer @ file:///tmp/build/80754af9/snowballstemmer_1611258885636/work sortedcollections @ file:///tmp/build/80754af9/sortedcollections_1611172717284/work sortedcontainers @ file:///tmp/build/80754af9/sortedcontainers_1623949099177/work soupsieve @ file:///tmp/build/80754af9/soupsieve_1616183228191/work Sphinx==4.2.0 sphinxcontrib-applehelp @ file:///home/ktietz/src/ci/sphinxcontrib-applehelp_1611920841464/work sphinxcontrib-devhelp @ file:///home/ktietz/src/ci/sphinxcontrib-devhelp_1611920923094/work sphinxcontrib-htmlhelp @ file:///tmp/build/80754af9/sphinxcontrib-htmlhelp_1623945626792/work sphinxcontrib-jsmath @ file:///home/ktietz/src/ci/sphinxcontrib-jsmath_1611920942228/work sphinxcontrib-qthelp @ file:///home/ktietz/src/ci/sphinxcontrib-qthelp_1611921055322/work sphinxcontrib-serializinghtml @ file:///tmp/build/80754af9/sphinxcontrib-serializinghtml_1624451540180/work sphinxcontrib-websupport @ file:///tmp/build/80754af9/sphinxcontrib-websupport_1597081412696/work spyder @ file:///C:/ci/spyder_1636480369575/work spyder-kernels @ file:///C:/ci/spyder-kernels_1634237096710/work SQLAlchemy @ file:///C:/ci/sqlalchemy_1626948551162/work squarify==0.4.3 statsmodels @ file:///C:/b/abs_bdqo3zaryj/croot/statsmodels_1676646249859/work sweetviz==2.1.4 sympy @ file:///C:/ci/sympy_1635219088507/work tables==3.6.1 tabulate @ file:///C:/ci/tabulate_1657619055201/work TBB==0.2 tblib @ file:///Users/ktietz/demo/mc3/conda-bld/tblib_1629402031467/work tenacity @ file:///C:/Windows/TEMP/abs_980d07a6-8e21-4174-9c17-7296219678ads7dhdov_/croots/recipe/tenacity_1657899108023/work terminado==0.9.4 testpath @ file:///tmp/build/80754af9/testpath_1624638946665/work text-unidecode @ file:///Users/ktietz/demo/mc3/conda-bld/text-unidecode_1629401354553/work textdistance @ file:///tmp/build/80754af9/textdistance_1612461398012/work threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work three-merge @ file:///tmp/build/80754af9/three-merge_1607553261110/work tifffile @ file:///tmp/build/80754af9/tifffile_1627275862826/work tinycss @ file:///tmp/build/80754af9/tinycss_1617713798712/work tldextract @ file:///opt/conda/conda-bld/tldextract_1646638314385/work toml @ file:///tmp/build/80754af9/toml_1616166611790/work toolz @ file:///home/linux1/recipes/ci/toolz_1610987900194/work tornado @ file:///C:/ci/tornado_1606924294691/work tqdm @ file:///tmp/build/80754af9/tqdm_1635330843403/work traitlets @ file:///tmp/build/80754af9/traitlets_1632522747050/work Twisted @ file:///C:/Windows/Temp/abs_ccblv2rzfa/croots/recipe/twisted_1659592764512/work twisted-iocpsupport @ file:///C:/ci/twisted-iocpsupport_1646798932792/work typed-ast @ file:///C:/ci/typed-ast_1624953797214/work typing-extensions @ file:///tmp/build/80754af9/typing_extensions_1631814937681/work ujson @ file:///C:/ci/ujson_1611259568517/work unicodecsv==0.14.1 Unidecode @ file:///tmp/build/80754af9/unidecode_1614712377438/work urllib3==1.26.7 w3lib @ file:///Users/ktietz/demo/mc3/conda-bld/w3lib_1629359764703/work watchdog @ file:///C:/ci/watchdog_1624955113064/work wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work webencodings==0.5.1 Werkzeug @ file:///tmp/build/80754af9/werkzeug_1635505089296/work whichcraft @ file:///tmp/build/80754af9/whichcraft_1617751293875/work widgetsnbextension @ file:///C:/ci/widgetsnbextension_1607531582688/work win-inet-pton @ file:///C:/ci/win_inet_pton_1605306162074/work win-unicode-console==0.5 wincertstore==0.2 wordcloud==1.8.2.2 wrapt @ file:///C:/ci/wrapt_1607574570428/work xarray @ file:///C:/b/abs_2fi_umrauo/croot/xarray_1668776806973/work xgboost==1.7.5 xlrd @ file:///tmp/build/80754af9/xlrd_1608072521494/work XlsxWriter @ file:///tmp/build/80754af9/xlsxwriter_1628603415431/work xlwings==0.24.9 xlwt==1.3.0 xmltodict @ file:///Users/ktietz/demo/mc3/conda-bld/xmltodict_1629301980723/work yapf @ file:///tmp/build/80754af9/yapf_1615749224965/work zict==2.0.0 zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work zope.event==4.5.0 zope.interface @ file:///C:/ci/zope.interface_1625036252485/work
#save file in export
!pip freeze > export/requirement.txt
WARNING: Ignoring invalid distribution -tatsmodels (c:\users\acer\anaconda3\lib\site-packages)